Relevant bibliographies by topics / Linear Convolution

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Linear Convolution'

Author: Grafiati
Published: 3 June 2025
Last updated: 19 July 2025

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Journal articles on the topic "Linear Convolution"

1

Narasimha, Madihally J. "Linear Convolution Using Skew-Cyclic Convolutions." IEEE Signal Processing Letters 14, no. 3 (2007): 173–76. http://dx.doi.org/10.1109/lsp.2006.884034.

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Pupeikis, Rimantas. "Revised linear convolution." Lietuvos matematikos rinkinys 60 (November 12, 2019): 33–38. http://dx.doi.org/10.15388/lmr.a.2019.14959.

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It is assumed that linear time-invariant (LTI) system input signal samples are updated by a sensor in real time. It is urgent for every new input sample or for small part of new samples to update an ordinary convolution as well. The idea is that well-known convolution sum algorithm, used to calculate output signal, should not be recalculated with every new input sample. It is necessary just to modify the algorithm, when the new input sample renew the set of previous samples. Approaches in time and frequency domains are analyzed. An example of computation of the convolution in time area is pres
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Zhang, Xin, Yingze Song, Tingting Song, et al. "LDConv: Linear deformable convolution for improving convolutional neural networks." Image and Vision Computing 149 (September 2024): 105190. http://dx.doi.org/10.1016/j.imavis.2024.105190.

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Stone, H. S. "Convolution theorems for linear transforms." IEEE Transactions on Signal Processing 46, no. 10 (1998): 2819–21. http://dx.doi.org/10.1109/78.720385.

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Kurakin, V. L. "Convolution of linear recurrent sequences." Russian Mathematical Surveys 48, no. 4 (1993): 249–50. http://dx.doi.org/10.1070/rm1993v048n04abeh001060.

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Moyo, Lloyd Edgar S. "Codomains for the Cauchy-Riemann and Laplace operators inℝ2". Journal of Function Spaces and Applications 6, № 1 (2008): 71–87. http://dx.doi.org/10.1155/2008/837415.

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A codomain for a nonzero constant-coefficient linear partial differential operatorP(∂)with fundamental solutionEis a space of distributionsTfor which it is possible to define the convolutionE*Tand thus solving the equationP(∂)S=T. We identify codomains for the Cauchy-Riemann operator inℝ2and Laplace operator inℝ2. The convolution is understood in the sense of theS′-convolution.
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Dharani Sri, Vuyyuru. "Road Extraction from Remote Sensing Images Using a Skip-Connected Parallel CNN-Transformer Encoder-Decoder Model." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem49159.

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Abstract - Extracting roads from remote sensing images holds significant practical value across fields like urban planning, traffic management, and disaster monitoring. Current Convolutional Neural Network (CNN) methods, praised for their robust local feature learning enabled by inductive biases, deliver impressive results. However, they face challenges in capturing global context and accurately extracting the linear features of roads due to their localized receptive fields. To address these shortcomings of traditional methods, this paper proposes a novel parallel encoder architecture that int
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Hari Kishore, K., Fazal Noorbasha, Katta Sandeep, D. N. V. Bhupesh, SK Khadar Imran, and K. Sowmya. "Linear convolution using UT Vedic multiplier." International Journal of Engineering & Technology 7, no. 2.8 (2018): 409. http://dx.doi.org/10.14419/ijet.v7i2.8.10471.

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Linear Convolution is one of the elemental operations of Signal processing systems and is used by some Multiplication Algorithms. In our project we perform Linear Convolution using ancient Multiplication Algorithm called UrdhvaTriyagbhyam (UT) which is one among the 16 sutras in Vedic mathematics. This provides best results in speed when compared to other multipliers. UrdhvaTriyagbhyam technique is used to increase the timing performance of the design. Our aim is to design 8 bit convolution using UT. The synthesis and simulation is done by using XILINX ISE Design.
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Li, Shang, Fei Yu, Shankou Zhang, Huige Yin, and Hairong Lin. "Optimization of Direct Convolution Algorithms on ARM Processors for Deep Learning Inference." Mathematics 13, no. 5 (2025): 787. https://doi.org/10.3390/math13050787.

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In deep learning, convolutional layers typically bear the majority of the computational workload and are often the primary contributors to performance bottlenecks. The widely used convolution algorithm is based on the IM2COL transform to take advantage of the highly optimized GEMM (General Matrix Multiplication) kernel acceleration, using the highly optimized BLAS (Basic Linear Algebra Subroutine) library, which tends to incur additional memory overhead. Recent studies have indicated that direct convolution approaches can outperform traditional convolution implementations without additional me
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Chen, Lei, Le Wu, Richang Hong, Kun Zhang, and Meng Wang. "Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 27–34. http://dx.doi.org/10.1609/aaai.v34i01.5330.

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Graph Convolutional Networks~(GCNs) are state-of-the-art graph based representation learning models by iteratively stacking multiple layers of convolution aggregation operations and non-linear activation operations. Recently, in Collaborative Filtering~(CF) based Recommender Systems~(RS), by treating the user-item interaction behavior as a bipartite graph, some researchers model higher-layer collaborative signals with GCNs. These GCN based recommender models show superior performance compared to traditional works. However, these models suffer from training difficulty with non-linear activation
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Dissertations / Theses on the topic "Linear Convolution"

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Hofmann, B., and G. Fleischer. "Stability Rates for Linear Ill-Posed Problems with Convolution and Multiplication Operators." Universitätsbibliothek Chemnitz, 1998. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-199800987.

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In this paper we deal with the `strength' of ill-posedness for ill-posed linear operator equations Ax = y in Hilbert spaces, where we distinguish according_to_M. Z. Nashed [15] the ill-posedness of type I if A is not compact, but we have R(A) 6= R(A) for the range R(A) of A; and the ill-posedness of type II for compact operators A: From our considerations it seems to follow that the problems with noncompact operators A are not in general `less' ill-posed than the problems with compact operators. We motivate this statement by comparing the approximation and stability behaviour of discrete leas
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Manrique, Tito. "Functional linear regression models : application to high-throughput plant phenotyping functional data." Thesis, Montpellier, 2016. http://www.theses.fr/2016MONTT264/document.

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L'Analyse des Données Fonctionnelles (ADF) est une branche de la statistique qui est de plus en plus utilisée dans de nombreux domaines scientifiques appliqués tels que l'expérimentation biologique, la finance, la physique, etc. Une raison à cela est l'utilisation des nouvelles technologies de collecte de données qui augmentent le nombre d'observations dans un intervalle de temps.Les jeux de données fonctionnelles sont des échantillons de réalisations de fonctions aléatoires qui sont des fonctions mesurables définies sur un espace de probabilité à valeurs dans un espace fonctionnel de dimensio
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Zhakupov, Mansur. "Application of convolution and average pressure approximation for solving non-linear flow problems. constant pressure inner boundary condition for gas flow." Texas A&M University, 2003. http://hdl.handle.net/1969.1/3937.

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The accurate description of fluid flow through porous media allows an engineer to properly analyze past behavior and predict future reservoir performance. In particular, appropriate mathematical models which describe fluid flow through porous media can be applied to well test and production data analysis. Such applications result in estimating important reservoir properties such as formation permeability, skin-factor, reservoir size, etc. "Real gas" flow problems (i.e., problems where the gas properties are specifically taken as implicit functions of pressure, temperature, and composition) are
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Mendonça, Marcelo de Barros. "Aplicação de texturas em visualização científica." Universidade de São Paulo, 2001. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-24042002-135403/.

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A crescente disponibilidade de recursos computacionais para o cálculo, simulação e aquisição de dados permite que cientistas e engenheiros produzam enormes conjuntos de dados, bi ou tridimensionais, em geral multivariados. A aplicação de técnicas de Computação Gráfica com o objetivo de ganhar compreensão desses dados compreende o objeto de estudo da área conhecida por Visualização Científica. Texturização é uma forma de variar as propriedades de uma superfície ponto a ponto de forma que esta simule detalhes que não estão de fato presentes na sua geometria. A texturização pode ser aplicada usa
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Wang, Zipeng. "Generalized Dirichlet to Neumann map for linear evolution equations on half-line, and, Singular integrals of non-convolution type on product spaces." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709335.

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Jouhet, Laurent. "Algorithmique du Network Calculus." Phd thesis, Ecole normale supérieure de lyon - ENS LYON, 2012. http://tel.archives-ouvertes.fr/tel-00776381.

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Le Network Calculus est une théorie visant à calculer des bornes pire-cas sur les performances des réseaux de communication. Le réseau est modélisé par un graphe orienté où les noeuds représentent des serveurs, et les flux traversant le réseau doivent suivre les arcs. S'ajoutent à cela des contraintes sur les courbes de trafic (la quantité de données passées par un point depuis la mise en route du réseau) et sur les courbes de service (la quantité de travail fournie par chaque serveur). Pour borner les performances pire-cas, comme la charge en différents points ou les délais de bout en bout, c
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Le, Ber Catherine. "Simulation sur ordinateur d'un système linéaire continu et stationnaire." Brest, 1988. http://www.theses.fr/1988BRES2020.

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Presentation de methodes d'estimation pour la sortie d'un systeme continu lorsque l'on dispose seulement des echantillons du signal d'entree. On montre comment definir un systeme discret dont la sortie est egale aux echantillons du systeme analogique pour une classe donnee de signaux d'entree. Developpement d'une nouvelle approche, appelee convolution recursive cascade, ou la connaissance des poles de la fonction de transfert continue lineaire est necessaire
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Masucci, Antonia Maria. "Moments method for random matrices with applications to wireless communication." Thesis, Supélec, 2011. http://www.theses.fr/2011SUPL0011/document.

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Dans cette thèse, on étudie l'application de la méthode des moments pour les télécommunications. On analyse cette méthode et on montre son importance pour l'étude des matrices aléatoires. On utilise le cadre de probabilités libres pour analyser cette méthode. La notion de produit de convolution/déconvolution libre peut être utilisée pour prédire le spectre asymptotique de matrices aléatoires qui sont asymptotiquement libres. On montre que la méthode de moments est un outil puissant même pour calculer les moments/moments asymptotiques de matrices qui n'ont pas la propriété de liberté asymptotiq
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Harper, Zen Michael. "Hankel operators, convolutions and other operators related to linear systems." Thesis, University of Leeds, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.411145.

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Kim, Kyung-Mi. "Test Vectors of Rankin-Selberg Convolutions for General Linear Groups." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1279896776.

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Books on the topic "Linear Convolution"

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O'Halloran, Patrick. A convolution based approach for simulating linear circuit blocks defined in the frequency domain within a nonlinear time domain simulator. University College Dublin, 1997.

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Bozhinov, Nikolai. Convolutional representations of commutants and multipliers. Publishing House of the Bulgarian Academy of Sciences, 1988.

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Bozhinov, Nikolai. Convolutional representations of commutants and multipliers. Pub. House of the Bulgarian Academy of Sciences, 1988.

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United States. National Aeronautics and Space Administration., ed. Study of one- and two-dimensional filtering and deconvolution algorithms for a streaming array computer: Final report. National Aeronautics and Space Administration, 1985.

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United States. National Aeronautics and Space Administration., ed. Study of one- and two-dimensional filtering and deconvolution algorithms for a streaming array computer: Final report, appendix 5. National Aeronautics and Space Administration, 1985.

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United States. National Aeronautics and Space Administration., ed. Study of one- and two-dimensional filtering and deconvolution algorithms for a streaming array computer: Final report, appendix 5. National Aeronautics and Space Administration, 1985.

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United States. National Aeronautics and Space Administration., ed. Study of one- and two-dimensional filtering and deconvolution algorithms for a streaming array computer: Final report : [appendices]. National Aeronautics and Space Administration, 1985.

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Böttcher, Albrecht. Convolution Operators and Factorization of Almost Periodic Matrix Functions. Springer, 2012.

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Böttcher, Albrecht, Yuri I. Karlovich, and Ilya M. Spitkovsky. Convolution Operators and Factorization of Almost Periodic Matrix Functions. Birkhauser Verlag, 2012.

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Böttcher, Albrecht, Yuri I. Karlovich, and Ilya M. Spitkovsky. Convolution Operators and Factorization of Almost Periodic Matrix Functions (Operator Theory: Advances and Applications). Birkhäuser Basel, 2002.

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Book chapters on the topic "Linear Convolution"

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Hörmander, Lars. "Convolution." In The Analysis of Linear Partial Differential Operators I. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-96750-4_5.

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Hörmander, Lars. "Convolution." In The Analysis of Linear Partial Differential Operators I. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-61497-2_5.

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Guan, Lei. "FPGA-based Linear Convolution." In FPGA-based Digital Convolution for Wireless Applications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52000-1_3.

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Tolimieri, R., Myoung An, and Chao Lu. "Linear and Cyclic Convolution." In Signal Processing and Digital Filtering. Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4757-3854-4_6.

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Hörmander, Lars. "Convolution Equations." In The Analysis of Linear Partial Differential Operators II. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/3-540-26964-9_8.

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Gibson, Jerry D. "Linear Systems, Convolution, and Filtering." In Synthesis Lectures on Communications. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19580-8_3.

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Guan, Lei. "Advanced FPGA-based Fast Linear Convolution." In FPGA-based Digital Convolution for Wireless Applications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52000-1_5.

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Heideman, Michael T. "Multiplicative Complexity of Linear and Bilinear Systems." In Multiplicative Complexity, Convolution, and the DFT. Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4612-3912-3_2.

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Beffa, Federico. "Convolution of Distributions." In Weakly Nonlinear Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40681-2_3.

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AbstractThe convolution product plays a central role in the description of linear and weakly-nonlinear systems. In this chapter we develop its theory based on distributions. In addition, the chapter introduces the tensor product which will also come to play an important role in the description of weakly-nonlinear systems.
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Sanders, J. W. "Linear Systems, Filters and Convolution Theorems." In Fourier Techniques and Applications. Springer US, 1985. http://dx.doi.org/10.1007/978-1-4613-2525-3_2.

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Conference papers on the topic "Linear Convolution"

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Wirth-Singh, Anna, Jinlin Xiang, Minho Choi, et al. "Compressed Meta-Optical Encoder for Image Classification." In CLEO: Fundamental Science. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_fs.2024.ff1j.1.

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For MNIST image classification, we compress a CNN to a single convolutional layer and linear electronic backend. We implement the convolution optically via engineering the PSF of meta-optics and demonstrate classification accuracy exceeding 93%.
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Yuchang, Zhu, and Xiao Nanfeng. "Lane Detection Method Based on Deformable Linear Convolution." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650782.

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Tenagyei, Edwin Kwadwo, Yongsheng Gao, Andrew Lewis, Nick Nikzad, and Jun Zhou. "LinCNNFormer: Hybrid Linear Vision Transformer Based on Convolution Neural Network and Linear Attention." In 2024 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2024. https://doi.org/10.1109/dicta63115.2024.00024.

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Pettanice, Giuseppe, Roberto Valentini, Piergiuseppe Di Marco, Fortunato Santucci, and Giulio Antonini. "Semi-Recursive Convolution Schemes for Macromodeling of Time-Invariant Linear Circuits." In 2025 19th European Conference on Antennas and Propagation (EuCAP). IEEE, 2025. https://doi.org/10.23919/eucap63536.2025.10999969.

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Wang, Tao, Wenliang Sun, Runze Ma, and Wei He. "A Bearing Fault Diagnosis Model Based on SCSA-CNN-LSTM with Linear Deformable Convolution." In 2024 6th International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI). IEEE, 2024. https://doi.org/10.1109/ricai64321.2024.10911210.

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Gangopadhyay, Udisha, Sudip Ghosh, and Hafizur Rahaman. "Low-Power Multiplier-Less VLSI Design and Implementation of Linear Convolution Using Vedic Mathematics." In 2025 Devices for Integrated Circuit (DevIC). IEEE, 2025. https://doi.org/10.1109/devic63749.2025.11012393.

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Wang, Nan, and Bingzhao Li. "The New Kind of Convolution and Correlation Theorems Associated with the Linear Canonical Wavelet Transform." In 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP). IEEE, 2024. http://dx.doi.org/10.1109/icsp62122.2024.10743888.

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Li, Tao, Zhengli Zhang, Ligang Li, and Guozheng Du. "A Model Integrating Wavelet Convolution and Linear Attention Mechanism for Inventory Management in Power Warehouses." In 2024 IEEE 4th International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA). IEEE, 2024. https://doi.org/10.1109/iciba62489.2024.10867921.

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Liu, Rongjie, and Gonghao Duan. "Long Time Series Forecasting Based on Dilated Causal Convolution and Focused Linear Attention with Informer." In 2024 3rd International Conference on Cloud Computing, Big Data Application and Software Engineering (CBASE). IEEE, 2024. https://doi.org/10.1109/cbase64041.2024.10824593.

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Li, Changli, Hon Keung Kwan, and Xinxin Qin. "Revisiting Linear Convolution, Circular Convolution and Their Related Methods." In 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2020. http://dx.doi.org/10.1109/cisp-bmei51763.2020.9263607.

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Reports on the topic "Linear Convolution"

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Ferdaus, Md Meftahul, Mahdi Abdelguerfi, Elias Ioup, et al. KANICE : Kolmogorov-Arnold networks with interactive convolutional elements. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49791.

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We introduce KANICE, a novel neural architecture that combines Convolutional Neural Networks (CNNs) with Kolmogorov-Arnold Network (KAN) principles. KANICE integrates Interactive Convolutional Blocks (ICBs) and KAN linear layers into a CNN framework. This leverages KANs’ universal approximation capabilities and ICBs’ adaptive feature learning. KANICE captures complex, non-linear data relationships while enabling dynamic, context-dependent feature extraction based on the Kolmogorov-Arnold representation theorem. We evaluated KANICE on four datasets: MNIST, Fashion-MNIST, EMNIST, and SVHN, compa
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Alzaid, Adulhamid A., Ka-Sing Lau, C. R. Rao, and D. N. Shanbhag. Solution of Deny Convolution Equation Restricted to Half Line Via a Random Approach. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada176951.

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Downard, Alicia, Stephen Semmens, and Bryant Robbins. Automated characterization of ridge-swale patterns along the Mississippi River. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40439.

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The orientation of constructed levee embankments relative to alluvial swales is a useful measure for identifying regions susceptible to backward erosion piping (BEP). This research was conducted to create an automated, efficient process to classify patterns and orientations of swales within the Lower Mississippi Valley (LMV) to support levee risk assessments. Two machine learning algorithms are used to train the classification models: a convolutional neural network and a U-net. The resulting workflow can identify linear topographic features but is unable to reliably differentiate swales from o
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Mosalam, Khalid, Issac Pang, and Selim Gunay. Towards Deep Learning-Based Structural Response Prediction and Ground Motion Reconstruction. Pacific Earthquake Engineering Research Center, 2025. https://doi.org/10.55461/ipos1888.

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This research presents a novel methodology that uses Temporal Convolutional Networks (TCNs), a state-of-the-art deep learning architecture, for predicting the time history of structural responses to seismic events. By leveraging accelerometer data from instrumented buildings, the proposed approach complements traditional structural analysis models, offering a computationally efficient alternative to nonlinear time history analysis. The methodology is validated across a broad spectrum of structural scenarios, including buildings with pronounced higher-mode effects and those exhibiting both line
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